In the insurance sector, Asset Liability Management refers to the joint management of the assets and liabilities of a company. The liabilities mainly consist of the policies portfolios of the insurance company, which usually contain a large amount of policies. In the article, the authors mainly develop a highly efficient automatic generation of model points portfolios to represent much larger real policies portfolios. The obtained model points portfolio must retain the market risk properties of the initial portfolio. For this purpose, the authors propose a risk measure that incorporates the uncertain evolution of interest rates to the portfolios of life insurance policies, following Ferri (Optimal model points portfolio in life, 2019, arXiv:1808.00866 ). This problem can be formulated as a minimization problem that has to be solved using global numerical optimization algorithms. The cost functional measures an appropriate distance between the original and the model point portfolios. In order to solve this problem in a reasonable computing time, sequential implementations become prohibitive, so that the authors speed up the computations by developing a high performance computing framework that uses hybrid architectures, which consist of multi CPUs together with accelerators (multi GPUs). Thus, in graphic processor units (GPUs) the evaluation of the cost function is parallelized, which requires a Monte Carlo method. For the optimization problem, the authors compare a metaheuristic stochastic differential evolution algorithm with a multi path variant of hybrid global optimization Basin Hopping algorithms, which combines Simulated Annealing with gradient local searchers (Ferreiro et al. in Appl Math Comput 356:282–298, 2019a). Both global optimizers are parallelized in a multi CPU together with a multi GPU setting.

中文翻译：

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使用多 CPU 和多 GPU 通过并行全局优化在保险中进行有效的模型点选择

在保险行业，资产负债管理是指对一家公司的资产和负债进行联合管理。负债主要包括保险公司的保单组合，通常包含大量保单。在文章中，作者主要开发了一种高效自动生成模型点组合来表示更大的真实政策组合。获得的模型点投资组合必须保留初始投资组合的市场风险属性。为此，作者提出了一种风险衡量标准，将利率的不确定演变纳入人寿保险单的投资组合，遵循 Ferri（生命中的最佳模型点投资组合，2019，arXiv：1808.00866）。这个问题可以表述为一个最小化问题，必须使用全局数值优化算法来解决。成本函数测量原始点组合和模型点组合之间的适当距离。为了在合理的计算时间内解决这个问题，顺序实现变得令人望而却步，因此作者通过开发使用混合架构的高性能计算框架来加速计算，该架构由多 CPU 和加速器（多 GPU）组成。因此，在图形处理器单元 (GPU) 中，成本函数的评估是并行化的，这需要蒙特卡罗方法。对于优化问题，作者将元启发式随机差分进化算法与混合全局优化 Basin Hopping 算法的多路径变体进行了比较，后者将模拟退火与梯度局部搜索相结合（Ferreiro 等人在 Appl Math Comput 356:282–298, 2019a 中）。两个全局优化器都在多 CPU 和多 GPU 设置中并行化。